Investigation on the Thomas Fire Impacts in Santa Barbara County, CA (2017 - 2018)

Leveraging MPC Landsat Collection 2 level-2 data & County Level AQI reports to assess burned areas and public health conditions.

Author: Sofia Ingersoll

Published: 2023-11-12

🗃️GitHub Repo: https://github.com/saingersoll/Thomas_Fire_Investigation

Google earth V 6.2.2.6613. (December 13, 2017). Santa Barbara, United States. 34.6099° N, 120.0665° W, Eye alt 13.72 feet. DigitalGlobe 2020. http://www.earth.google.com [December 12, 2023].

The Thomas Fire

On December 4, 2017, a Santa Ana wind event with historic duration swept throughout Ventura and Santa Barbara County with an average speed of 32 m/s and early gusts as high as 35 m/s (Fovell, Gallagher, 2018). Southern California's Mediterranean climate creates an abundance of ample fire-fuel during the highest stress period for vegetation, typically between July and the first rain fall following summer (Nauslar, Abatglou, Marsh, 2018). As a result of frequent, unprecedented wildfires invasive weeds and grasses, also known as "flashy fuels", dominate large land parcels making them more fire prone (National Parks Service, 2023). These combined factors, alongside weakened and outdated electrical infrastructure introduce high wildfire risks throughout large portions of the state. Soon after the Santa Ana winds picked up, a "line slap," was shared between Southern California Edison ("SCE") powerlines, sparking hot materials which then ignited a nearby fire-fuel bed (Ventura County Fire Department, 2019). This rapidly developed into the Thomas Fire, and within hours, the same circuit was also responsible for the Woolsey Fire, and Koeningstein Fire (Ventura County Fire Department, 2019).

All together, these fires devistated both Ventura and Santa Barbara County for a total of 40 days. The Thomas Fire alone consumed total of 281,893 acres; destroying 1,063 structures and claiming two casualties (one civilian and one firefighter) (CAL FIRE, n.d.). Though, even greater losses occured in January 2018, as a result of mudslides in the areas impacted by the fire. Unfortunately, 23 lives were lost due to debris flow in Montecito (Wildfire Today, n.d.; California Govenor's Office of Emergency Services, n.d.). As of 2019, SCE agreed to a $360 million settlement to address the conglomorate negative impacts caused by the Thomas Fire, Woolsey Fire, and Koeningstein Fire. To help reduce wildfire risks and prevent a similar trajedies, utility companies across the state have invested in strucutral hardening (Lazo, 2024). Although, that is not the only proactive wildfire mitigation tactic being exercised. Members of the USDA Forest Service and the California Air Resources Board (CARB) are devoted to identifying areas with surplus fire-fuel accumulation to conduct prescribed ("Rx") burns. Further implementation of proactive land managment practicies can strengthen California's resiliency against extreme and unprecedented weather events. Thus securing greater safety and security for its natural spaces, structures, and residents.

Objective

To gain a better understanding of the initial environmental and public health impacts primarily caused by the Thomas Fire, we will:

• Quanitfy and visualize the amount of wildfire air pollution in Santa Barbara County between 2017/01 - 2018/10 using the Daily AQI and the average AQI over a 5 day rolling window in units of ppm.

• Examine the burn scar using false-color imaging (red, green, blue, near-infrared and shortwave infrared bands) from the Landsat-2 Collection 2 Level-2 atmosperically corrected surface reflectance satellite data from the Microsoft Planetary Computer ("MPC").

Outcome

The figure on the left displays the mean AQI ratings with respect to the AQI Health Guidelines. A sharp peak is observed the day the Thomas Fire began (Dec. 4, 2017) which falls within the Very Unhealthy category. Whereas the a 5 day period average was primairly in the Unhealthy category. This trend can be observed at the noted location in the figure below, indicating negative public health effects on air quality resulting from the Thomas Fire in combination with neighboring fires. The figure on the right, displays the large parcel of Santa Barbara County that was engulfed by the fire, explaining the uptake in particulate matter observed in December of 2017. There were detrimental direct impacts on public health and safety in terms of air quality as a response to these fires.

Techniques Applied

• Directly accessing & processing MPC STAC data
• Raster analysis applying false color imagery
• Time series analysis

Data Access

AQI Data for PPM Quantification & Trend Analysis Overtime

The Daily Air Quality Index (AQI) data to quantify the particulate matter released into Santa Barbara County from the fire was collected here from the US Environmental Protection Agency to visualize the rolling AQI averages between 2017 and 2018.

Landsat Data for Burn Scar Visualization

For our true and false color imagery, we are going to direct access Microsoft Planetary Computer Landsat Collection 2 Level-2 data. The STAC item utilized for this project is ****LE07_L2SP_042036_20171217_02_T1****. The raster data was collected on 2017-12-17.

This data should be used for visualization purposes only.

California Fire Perimeter Data for Burn Scar Visualization

The shapefile of fire perimeters in California were provided by the California State Geoportal. The complete file can be accessed here.

Data References

• US Environmental Protection Agency (2023). Daily AQI by County [Data File]. Available from https://aqs.epa.gov/aqsweb/airdata/download_files.html#AQI. Accessed October 25, 2023

• Microsoft Planetary Computer. Landsat Collection 2 Level-2 [Dataset]. Available from https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2. Accessed November 28, 2023

• California Department of Forestry and Fire Protection (2023). California Fire Perimeters (all) [Data File]. Available from https://gis.data.ca.gov/datasets/CALFIRE-Forestry::california-fire-perimeters-all-1/about. Accessed November 28, 2023

Software Specifications

#------------------------------------
# ----    Load the Essentials    ----
#------------------------------------
# Reading in libraries and functions
import os
import warnings
import numpy as np
import pandas as pd

import pystac
import planetary_computer

import rasterio
import xarray as xr
import geopandas as gpd
import rioxarray as rioxr
from shapely.geometry import Polygon

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib.patches as mpatches

Read & Wrangle SB County AQI Data

To simplify our workflow, we're going to combine the 2017 and 2018 data sets, and wrangle a single concatonated dataset.

Once we have one dataset, we will select our region of interest (ROI) and correct the Date dtype so it may be used as the index to calculate the average Air Quality Index over a 5 day rolling window.

#------------------------------------
# ----       Read & Wrangle      ----
#------------------------------------
# Reading in the data
aqi_17 = pd.read_csv('https://aqs.epa.gov/aqsweb/airdata/daily_aqi_by_county_2017.zip')                     
aqi_18 = pd.read_csv('https://aqs.epa.gov/aqsweb/airdata/daily_aqi_by_county_2018.zip') 

# Glueing the datasets together
aqi = pd.concat([aqi_17, aqi_18])                                                                         
#  .str.replace(' ','_') to replace the space for _
aqi.columns = aqi.columns.str.lower().str.replace(' ','_')     

# Subsetting using loc
# selecting SB county
aqi_sb = aqi.loc[aqi.county_name == 'Santa Barbara']   
# isolating desired columns   
aqi_sb = aqi_sb.iloc[:, 4:]                               

# Datetime Indexing
# converting the date type to datetimes64
aqi_sb.date = pd.to_datetime(aqi_sb.date)    
 # updating the index to the data column                       
aqi_sb = aqi_sb.set_index('date')                                    

# Rolling Window Mean Calc
# provides rolling window calculations of 
# the mean aqi over 5 day periods  
aqi_sb['five_day_average'] = aqi_sb.aqi.rolling('5D').mean()         

Check-point

Is everything looking as we expect it to?

#------------------------------------
# ----        Check Point!       ----
#------------------------------------
# Checking that dataframes joined properly and column names changed
print('The number of aqi observations in 2017 were:', len(aqi_17.Date))
print('The number of aqi observations in 2018 were:', len(aqi_18.Date))
print('The number of aqi observations between 2017-2018 were:', len(aqi.date))        

The number of aqi observations in 2017 were: 326801
The number of aqi observations in 2018 were: 327541
The number of aqi observations between 2017-2018 were: 654342

AQI Visualization: Scatter Plot of 5 Day Average AQI Assessment, Santa Barbara County (Jan 2017 - Oct 2018)

The visual below displays the mean AQI over a 5 day rolling window between January 2017 and October 2018. The background of the plot is color categorized according to the AQI's six categories of concern for ppm levels. A relative trend of moderate is seen throughout the year. However, a spike in air pollutants between the months of December 2017 and January 2018 is clearly observed. The sharp initial peak observed the day the Thomas Fire began (Dec. 4, 2017) caused an inital AQI response within the Very Unhealthy category and over a 5 day period, averaged out into the Unhealthy category. This trend can be observed at the noted location in the figure below, indicating negative public health effects on air quality resulting from the Thomas Fire and other neighboring fires.

#------------------------------------
#  ----     Customization        ----
#------------------------------------
# Define AQI categories and colors
aqi_categories = {
    'Good': (0, 50, '#00E400'),
    'Moderate': (51, 100, '#FFFF00'),
    'Unhealthy for Sensitive Groups': (101, 150, '#FF9933'),
    'Unhealthy': (151, 200, '#FF0000'),
    'Very Unhealthy': (201, 300, '#8B0000'),
    'Hazardous': (301, 500, '#800080')
}

colors = {'aqi':'#f69517',
          'five_day_average':'#360F39'}


#------------------------------------
#  ----     Visualizing AQI      ----
#------------------------------------
fig, ax = plt.subplots(figsize=(12, 8))

# Plot AQI categories as background colors for associated ranges
for category, (lower, upper, color) in aqi_categories.items():
    ax.fill_between(aqi_sb.index, lower, upper, color=color,
                    alpha=0.2,
                    label=f'{category}')

# Plot the AQI and 5-Day Rolling Average
aqi_sb.plot(ax=ax,
            y=['aqi', 'five_day_average'],
            color=colors,
            xlabel='Date',                                                   
            ylabel='AQI Values (ppm)',
            ylim= (0,400),
            legend= True
            )

# applying customizations
ax.set_title('Air Quality Index (AQI) Assessment of Santa Barbara County 2017-2018', 
             fontsize=18) 

# add a legend for background colors
background_legend = [Line2D([0], [0], color='#00E400', lw=4, alpha=0.3),
                     Line2D([0], [0], color='#FFFF00', lw=4, alpha=0.3),
                     Line2D([0], [0], color='#FF9933', lw=4, alpha=0.3),
                     Line2D([0], [0], color='#FF0000', lw=4, alpha=0.3),
                     Line2D([0], [0], color='#8B0000', lw=4, alpha=0.3),
                     Line2D([0], [0], color='#800080', lw=4, alpha=0.3)]

# add background color legend to the plot
background_legend_art = ax.legend(handles=background_legend,
                                  labels=['Good', 'Moderate',
                                          'Unhealthy for Sensitive Groups',
                                          'Unhealthy', 'Very Unhealthy',
                                          'Hazardous'],
                                  loc='upper center',
                                  bbox_to_anchor=(0.5, 1),
                                  ncol=3,
                                  fontsize = 12)
# line color legend
line_legend = [Line2D([0], [0], color='#f69517', lw=2),
               Line2D([0], [0], color='#360F39', lw=2)]

# add line color legend to the plot
line_legend_art = ax.legend(handles=line_legend,
                            labels=['AQI', '5-Day Average'],
                            loc='upper right',
                            bbox_to_anchor=(0.866, 0.88),
                            fontsize = 12)

# Add both legends to the plot
ax.add_artist(background_legend_art)
ax.add_artist(line_legend_art)

# Add annotation
ax.annotate("This spike is a result of \nWildfire Exposure in Santa Barbara.",
            xy=(0.48, 0.5),  
            xycoords='figure fraction',
            fontsize=14,
            color='black',
            va='center')

# Add curly brace to the left of the annotation
ax.text(0.50, 0.57, '}',
        transform=fig.transFigure,
        fontsize=60,      
        va='center',
        ha='center',
        color='black')

# Adjust subplot parameters to add margin space
plt.subplots_adjust(top=0.85, bottom=0.15, left=0.1, right=0.9)

plt.show()

Read & Inspect Burn Scar Data

Now, let's visualize how much local area was affected by the Thomas Fire by importing the fire perimeter boundary information and Landsat 2.

CA Fire Perimeters

#------------------------------------
# ----        Read  & Inspect     ----
#------------------------------------
# Reading in the data for CA fire perimeters 
ca_fire = gpd.read_file(os.path.join(os.getcwd(),'..','data','California_Fire_Perimeters_1379327890478655659','California_Fire_Perimeters_(all).shp'))

#------------------------------------
# ----        Check Point!       ----
#------------------------------------
# Suppress warnings
warnings.filterwarnings('ignore', category=UserWarning)
warnings.filterwarnings('ignore', category=FutureWarning) 

# Create a figure with two subplots
fig, ax = plt.subplots(1, 2, figsize=(14, 7))

# Quick viuslaization of CA fire perimeters
# yow-za! CA is so clearly shaped }:<
ca_fire.plot(ax = ax[0],
             color = '#AA4203')
ax[0].set_title('CA Fire Perimeters')


# Subset for Thomas Fire boundary data for plotting
thomas_fire = ca_fire.loc[(ca_fire['FIRE_NAME'] == 'THOMAS') & (ca_fire['YEAR_'] >= 2017)]          


thomas_fire.plot(ax = ax[1],
                 color = '#AA4203')
ax[1].set_title('Thomas Fire Boundary')

# Adjust layout to prevent overlap
plt.tight_layout()

# Show the plot
plt.show()

MPC Landsat Collection 2 Level-2

Together, we're going to load in Landsat data directly from the Microsoft Planetary Computer STAC and collect our desired bands (SWIR22, NIR08, Red) to create a landsat array subset.LE07_L2SP_042036_20171217_02_T1 was captured on 12/17/2017 18:36:51 UTC and will be leveraged for this project.

In order to create a false color image, we need to adjust the dimensions of our data to only consider x and y coordinates. Furthermore, we will need to create an array containing the false color bands we intend on utilizing for our ROI. We'll also be correcting the CRS so we can overlay the two datasets.

#--------------------------------------
# ---- Pull directly from MPC STAC ----
#--------------------------------------
# Suppress warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)  


# Let's pull our data fresh from the MPC STAC
# we're also going to assign the bands we're interested in
item_url = "https://planetarycomputer.microsoft.com/api/stac/v1/collections/landsat-c2-l2/items/LE07_L2SP_042036_20171217_02_T1"

# Load the individual item metadata and sign the assets
item = pystac.Item.from_file(item_url)
signed_item = planetary_computer.sign(item)

#--------------------------------------
# ----   Collect band information  ----
#--------------------------------------
# Open the desired data assets
# Short Wave Infrared (SWIR22) band
asset_href_swir22 = signed_item.assets["swir22"].href
landsat_swir22 = rioxr.open_rasterio(asset_href_swir22)

# Near Infrared (NIR08) band
asset_href_nir08 = signed_item.assets["nir08"].href
landsat_nir08 = rioxr.open_rasterio(asset_href_nir08)

# Red band
asset_href_red = signed_item.assets["red"].href
landsat_red = rioxr.open_rasterio(asset_href_red)

# Green band
asset_href_green = signed_item.assets["green"].href
landsat_green = rioxr.open_rasterio(asset_href_green)

# Blue band
asset_href_blue = signed_item.assets["blue"].href
landsat_blue = rioxr.open_rasterio(asset_href_blue)

#--------------------------------------
#  ----  Combine band information  ---- 
#--------------------------------------
#--------------------------------------
#  ----         True Color         ---- 
#--------------------------------------
# Stack bands into a single dataset
true_color = xr.concat([landsat_red, landsat_green, landsat_blue], dim='band')

# Updating data for plotting
# original dimensions and coordinates show us that band is a dimension

# Remove length 1 dimension (band)
true_color = true_color.squeeze()

# remove coordinates associated to band
true_color = true_color.drop('band')

#--------------------------------------
#  ----        False Color         ---- 
#--------------------------------------
# Note: For false-color, typically, you might use 
# a different combination (like NIR, Red, Green)
false_color = xr.concat([landsat_swir22, landsat_nir08, landsat_red], dim='band')

# Updating data for plotting
# original dimensions and coordinates show us that band is a dimension

# Remove length 1 dimension (band)
false_color = false_color.squeeze()

# remove coordinates associated to band
false_color = false_color.drop('band')

#--------------------------------------
#  ---- Visualize band information ---- 
#--------------------------------------
# Create a figure with two subplots
fig, ax = plt.subplots(1, 2, figsize=(14, 7))

# Plot the true and false color images
#--------------------------------------
#  ----     True Color Image       ---- 
#--------------------------------------
# Plot the false color image
true_color.plot.imshow(ax=ax[0],
                        robust=True) 
ax[0].set_title('True Color Landsat Image (Red, Green, Blue)')


#--------------------------------------
#          False Color Image
#--------------------------------------
false_color.plot.imshow(ax=ax[1],
                        robust=True)
ax[1].set_title('False Color Landsat Image (SWIR22, NIR08, Red)')

plt.tight_layout()
plt.show()

Check-point

It looks good, let's take a quick peak at the geospatial attr

#------------------------------------
# ----        Check Point!       ----
#------------------------------------
# Check geospatial attributes
print('height: ', false_color.rio.height)
print('width: ', false_color.rio.width, '\n')
print(false_color.rio.bounds(), '\n')

# Now to update the CRS to match and check
# Convert DataFrame to GeoDataFrame
thomas_fire  = gpd.GeoDataFrame(thomas_fire, geometry='geometry')

thomas_fire = thomas_fire.to_crs(false_color.rio.crs)                                      

# Print CRS to check alignment
print('Thomas Fire Boundary CRS: ', thomas_fire.crs)
print('False Color CRS: ', false_color.rio.crs)

height:  7271
width:  8291 

(106785.0, 3725685.0, 355515.0, 3943815.0) 

Thomas Fire Boundary CRS:  PROJCS["WGS 84 / UTM zone 11N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-117],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32611"]]
False Color CRS:  EPSG:32611

Inspecting Areas Burned by Thomas Fire in Santa Barbara County (2017)

Below, we've overlayed the fire perimeter boundaries for the Thomas Fire over it's respective burn scar. Our false color image displays an outline of the fire scorn area of Santa Barbara County. Here, we can observe the severity of the damage by generally visualizing the area and the types of regions impacted by the wildfire.

#------------------------------------
# ----  Plot Landsat False Color ----
#------------------------------------
# Adjust the figure size and DPI
fig, ax = plt.subplots(figsize=(6, 6)) 

# Plot false color bands
false_color.plot.imshow(ax=ax, robust=True)

# Plot Thomas Fire burn area
thomas_fire.plot(ax=ax, color='none', edgecolor='#AA4203')

# Add a legend for the burned area
fire_scar = mpatches.Patch(color='#AA4203', label='Burned Area')
ax.legend(handles=[fire_scar], loc='upper right', fontsize=12) 

# Add a title to the plot
ax.set_title('False Color Image (SWIR22, NIR08, Red)', fontsize=14)  

# Remove the plot axes
ax.axis('off')

# Save the figure with tight bounding box and high resolution
plt.savefig('thomas_fire_plot.png')  
plt.show()

Combining the Visuals

Let's put the most informative visuals together into a single figure to gain a full sense of the extent of damage caused by the Thomas Fire. In AQI the figure on the left, there is a sharp peak observed the day the Thomas Fire began (Dec. 4, 2017) that fell within the Very Unhealthy category and over a 5 day period, averaged out into the Unhealthy category. This trend can be observed at the noted location in the figure below, indicating negative public health effects on air quality resulting from the Thomas Fire. The figure on the right, displays the large parcel of Santa Barbara County that was engulfed by the fire, explaining the uptake in particulate matter observed in December of 2017. There were significant direct impacts on public health in terms of air quality as a response to this fire and this can be understood when considering the size of the burn scar seen in the figure on the right.

#------------------------------------
#  ----     Customization        ----
#------------------------------------
# Define AQI categories and colors
aqi_categories = {
    'Good': (0, 50, '#00E400'),
    'Moderate': (51, 100, '#FFFF00'),
    'Unhealthy for Sensitive Groups': (101, 150, '#FF9933'),
    'Unhealthy': (151, 200, '#FF0000'),
    'Very Unhealthy': (201, 300, '#8B0000'),
    'Hazardous': (301, 500, '#800080')
}

colors = {'aqi':'#f69517',
          'five_day_average':'#360F39'}

# Plot the images in the same figure side-by-side
fig, (ax1, ax2) = plt.subplots(1, 2, 
                               figsize = (22,8)) 


#------------------------------------------------------------------------
# Plotting the Daily & 5-Day Rolling Window AQI for Santa Barbara County 
#------------------------------------------------------------------------
#------------------------------------
#  ----     Visualizing AQI      ----
#------------------------------------
# Plot AQI categories as background colors for associated ranges
for category, (lower, upper, color) in aqi_categories.items():
    ax1.fill_between(aqi_sb.index, lower, upper, color=color, alpha=0.2, label=f'{category}')

# Plot the AQI and 5-Day Rolling Average
aqi_sb.plot(ax=ax1,
            y=['aqi', 'five_day_average'],
            color=colors,             
            ylim= (0,400),
            legend= True,
            ylabel='AQI Values (ppm)'
            )

# applying customizations
ax1.set_title('Air Quality Index (AQI) Assessment of Santa Barbara County 2017-2018', 
             fontsize=21) 

ax1.set_xlabel(xlabel='Date',
               fontsize = 13)

ax2.set_ylabel(ylabel='AQI Values (ppm)',
               fontsize = 10)

# add a legend for background colors
background_legend = [Line2D([0], [0], color='#00E400', lw=6, alpha=0.3),
                     Line2D([0], [0], color='#FFFF00', lw=6, alpha=0.3),
                     Line2D([0], [0], color='#FF9933', lw=6, alpha=0.3),
                     Line2D([0], [0], color='#FF0000', lw=6, alpha=0.3),
                     Line2D([0], [0], color='#8B0000', lw=6, alpha=0.3),
                     Line2D([0], [0], color='#800080', lw=6, alpha=0.3)]

# add background color legend to the plot
background_legend_art = ax1.legend(handles=background_legend,
                                  labels=['Good', 'Moderate', 
                                          'Unhealthy for Sensitive Groups',
                                          'Unhealthy', 'Very Unhealthy',
                                          'Hazardous'],
                                  loc='upper center',
                                  bbox_to_anchor=(0.5, 1),
                                  ncol=3,
                                  fontsize = 18)
# line color legend
line_legend = [Line2D([0], [0], color='#f69517', lw=2),
               Line2D([0], [0], color='#360F39', lw=2)]

# add line color legend to the plot
line_legend_art = ax1.legend(handles=line_legend,
                            labels=['AQI', '5-Day Average'],
                            loc='upper right',
                            bbox_to_anchor=(1, 0.86),
                            fontsize = 18)

# Add both legends to the plot
ax1.add_artist(background_legend_art)
ax1.add_artist(line_legend_art)


# Add grouped annotation text (figure-level positioning)
fig.text(0.28, 0.55,
         "This spike is a result of \nWildfire exposure in Santa Barbara",
         fontsize=20,
         va='center',
         ha='left',
         color='black')

# Add curly brace to the left of the annotation
fig.text(0.27, 0.55, '}',
         fontsize=80,   # increase/decrease to span both peaks
         va='center',
         ha='center',
         color='black')
#------------------------------------------------------------------------
#        Plotting the Thomas Fire burned areas of Santa Barbara
#------------------------------------------------------------------------
 # Remove plot axes
plt.axis('off')                               
# Plot false color bands
false_color.plot.imshow(ax = ax2,              
                        # Include colors
                        robust = True)        

# Plot thomas fire burn area
thomas_fire.plot(ax = ax2,                     
       # no color of burn area
       color = 'none',                         
       # opacity of edgecolor
       edgecolor = '#AA4203')     
          
# Add a figure legend
fire_scar = mpatches.Patch(color = '#AA4203',
                          label = 'Burned Area') 

ax2.legend(handles=[fire_scar])

#------------------------------------
# ----   Plot the Fire Bound    -----
#------------------------------------
thomas_fire.plot(ax = ax2,
                 color = '#AA4203',
                 # make border around shapefile
                edgecolor = '#AA4203', 
                 # make transparent
                alpha = 0.5)

fire_patch = mpatches.Patch(color = '#AA4203',
                            label = "Thomas Fire")

#------------------------------------
# ----  Plot Landsat False Color ----
#------------------------------------
# Plot the false landsat
false_color.plot.imshow(ax = ax2,
                          robust = True) 

# Edit the Legend and Caption 
# show lables for legend
ax2.legend(handles = [fire_patch], 
        # no border around legend
          frameon = True,
        # where legend is located
          bbox_to_anchor = (0.9, 0.8),
          fontsize = 18) 
 
# Add title
ax2.set_title('Areas Burned by Thomas Fire in Santa Barbara, CA (2017)',
               fontsize = 21) 

# Plot the whole figure  
# space well
fig.tight_layout()
plt.show()

Citations:

1. AirNow.gov, U.S. EPA. (n.d.). Aqi Basics. AQI Basics | AirNow.gov. https://www.airnow.gov/aqi/aqi-basics/

2. CAL FIRE. (n.d.).Thomas Fire. (n.d.) https://www.fire.ca.gov/incidents/2017/12/4/thomas-fire/

3. CAL FIRE. (n.d.). California fire perimeters (all). California State Geoportal. https://gis.data.ca.gov/datasets/CALFIRE-Forestry::california-fire-perimeters-all-1/about

4. California Govenor's Office of Emergency Services. (n.d.). Montecito Mudslides Anniversary, Reflections Through Images | Cal OES News. https://news.caloes.ca.gov/montecito-mudslides-anniversary-reflections-through-images.

5. Fovell, R. & Gallagher, A. (2018). Winds and Gusts during the Thomas Fire. Fire. 01. 47. 10.3390/fire1030047. https://www.researchgate.net/publication/329331943_Winds_and_Gusts_during_the_Thomas_Fire

6. Lazo, A. (2024). Californians pay billions for power companies wildfire prevention efforts. Are they cost-effective? Cal Matters. https://calmatters.org/environment/2024/12/pge-utilities-wildfire-prevention-customer-bills-california/

7. Microsoft Planetary Computer. (n.d.). Planetary Computer. https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2

8. National Park Service. (n.d.) 2018 Woolsey Fire. Santa Monica Mountains National Recreation Area California.https://www.nps.gov/samo/learn/management/2018-woolsey-fire.htm

9. Nauslar, N. J., Abatzoglou, J. T., & Marsh, P. T. (2018). The 2017 North Bay and Southern California Fires: A Case Study. Fire, 1(1), 18. https://doi.org/10.3390/fire1010018

10. Ventura County Fire Department. (2019). Koenigstein Fire Cause Released. https://vcfd.org/news/vcfd-determines-cause-of-the-thomas-fire/

11. Ventura County Fire Department. (2019) VCFD Determines Cause of the Thomas Fire. Ventura County Fire Department. https://vcfd.org/news/vcfd-determines-cause-of-the-thomas-fire/

12. Wikipedia contributors. (2024) "Thomas Fire." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia. Web. Accessed 2 Aug. 2024.

13. Wildfire Today. (2019) Thomas Fire Archives - Wildfire Today. Wildfire Today. https://wildfiretoday.com/tag/thomas-fire.

Last Edited: 2026-2-11